Journal cover Journal topic
Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
Journal topic

Journal metrics

IF value: 5.153
IF5.153
IF 5-year value: 5.460
IF 5-year
5.460
CiteScore value: 7.8
CiteScore
7.8
SNIP value: 1.623
SNIP1.623
IPP value: 4.91
IPP4.91
SJR value: 2.092
SJR2.092
Scimago H <br class='widget-line-break'>index value: 123
Scimago H
index
123
h5-index value: 65
h5-index65
Preprints
https://doi.org/10.5194/hess-2020-43
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-2020-43
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

  17 Feb 2020

17 Feb 2020

Review status
This preprint is currently under review for the journal HESS.

A flexible two-stage approach for blending multiple satellite precipitation estimates and rain gauge observations: an experiment in the northeastern Tibetan Plateau

Yingzhao Ma1, Xun Sun2,3, Haonan Chen4, Yang Hong5, and Yinsheng Zhang6,7 Yingzhao Ma et al.
  • 1Colorado State University, Fort Collins, CO 80523, USA
  • 2Key Laboratory of Geographic Information Science (Ministry of Education), East China Normal University, Shanghai 200241, China
  • 3Columbia Water Center, Earth Institute, Columbia University, New York, NY 10027, USA
  • 4NOAA/Earth System Research Laboratory, Boulder, CO 80305, USA
  • 5School of Civil Engineering and Environmental Science, University of Oklahoma, Norman, OK 73019, USA
  • 6Key Laboratory of Tibetan Environment Changes and Land Surface Processes, Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing, 100101, China
  • 7CAS Center for Excellence in Tibetan Plateau Earth Sciences, Beijing, 100101, China

Abstract. Substantial biases exist in the Satellite Precipitation Estimates (SPE) over complex terrain regions and it has always been a challenge to quantify and correct such biases. The combination of multiple SPE and ground observations would be beneficial to improve the precipitation estimates. In this study, a flexible two-step approach is proposed by firstly reducing the systematic errors of each SPE using rain gauge observations as references, and then merging the improved multi-SPE with a Bayesian weighting model. In the 1st stage, gauge references are assumed as a generalized regression function of SPE and terrain feature. In the 2nd stage, the weights assigned to the involved SPE are calculated according to the associated performance relative to gauge references. This blending method has the ability to exert benefits from multi-SPE in terms of higher performance and mitigate negative impacts from the ones with lower quality. In addition, Bayesian analysis is applied in the two phases by specifying prior distributions on the model parameters, which enables to produce posterior ensembles associated with their predictive uncertainties. The performance of the two-step blending approach is assessed using independent rain gauge observations during the warm season of 2014 in the northeastern Tibetan Plateau. Results show that the blended multi-SPE are significantly improved compared to the original individuals, especially during heavy rainfall events. This study can also be expanded as a data fusion framework in the development of high-quality precipitation products in high-cold regions characterized by complex terrain.

Yingzhao Ma et al.

Interactive discussion

Status: final response (author comments only)
Status: final response (author comments only)
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
[Login for Authors/Editors] [Subscribe to comment alert] Printer-friendly Version - Printer-friendly version Supplement - Supplement

Yingzhao Ma et al.

Yingzhao Ma et al.

Viewed

Total article views: 467 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
329 122 16 467 23 22
  • HTML: 329
  • PDF: 122
  • XML: 16
  • Total: 467
  • BibTeX: 23
  • EndNote: 22
Views and downloads (calculated since 17 Feb 2020)
Cumulative views and downloads (calculated since 17 Feb 2020)

Viewed (geographical distribution)

Total article views: 382 (including HTML, PDF, and XML) Thereof 378 with geography defined and 4 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Saved

No saved metrics found.

Discussed

No discussed metrics found.
Latest update: 28 Sep 2020
Publications Copernicus
Download
Short summary
Substantial biases exist in the Satellite Precipitation Estimates (SPE) in complex terrain regions and it remains a challenge to quantify and correct such biases. This study proposes a two-step approach by firstly reducing the systematic errors of each SPE using rain gauge observations as references, and then merging the improved multi-SPE with a Bayesian weighting model. It is found that the merged multi-SPE are significantly improved compared to each SPE, especially in heavy rainfall events.
Substantial biases exist in the Satellite Precipitation Estimates (SPE) in complex terrain...
Citation